Koutsouleris, Nikolaos, Dwyer, Dominic B., Degenhardt, Franziska, Maj, Carlo, Urquijo-Castro, Maria Fernanda, Sanfelici, Rachele, Popovic, David, Oeztuerk, Oemer, Haas, Shalaila S., Weiske, Johanna, Ruef, Anne, Kambeitz-Ilankovic, Lana, Antonucci, Linda A., Neufang, Susanne, Schmidt-Kraepelin, Christian, Ruhrmann, Stephan, Penzel, Nora, Kambeitz, Joseph, Haidl, Theresa K., Rosen, Marlene, Chisholm, Katharine, Riecher-Rossler, Anita, Egloff, Laura, Schmidt, Andre, Andreou, Christina, Hietala, Jarmo, Schirmer, Timo, Romer, Georg, Walger, Petra, Franscini, Maurizia, Traber-Walker, Nina, Schimmelmann, Benno G., Fluckiger, Rahel, Michel, Chantal, Rossler, Wulf ORCID: 0000-0003-0049-4533, Borisov, Oleg, Krawitz, Peter M., Heekeren, Karsten ORCID: 0000-0001-5105-1922, Buechler, Roman, Pantelis, Christos, Falkai, Peter, Salokangas, Raimo K. R., Lencer, Rebekka, Bertolino, Alessandro, Borgwardt, Stefan, Noethen, Markus, Brambilla, Paolo, Wood, Stephen J., Upthegrove, Rachel, Schultze-Lutter, Frauke, Theodoridou, Anastasia and Meisenzahl, Eva . Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression. JAMA Psychiatry. CHICAGO: AMER MEDICAL ASSOC. ISSN 2168-6238

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Abstract

Importance Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures Accuracy and generalizability of prognostic systems. Results A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and Relevance These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation. Question Can a transition to psychosis be predicted in patients with clinical high-risk states or recent-onset depression by optimally integrating clinical, neurocognitive, neuroimaging, and genetic information with clinicians' prognostic estimates? Findings In this prognostic study of 334 patients and 334 control individuals, machine learning models sequentially combining clinical and biological data with clinicians' estimates correctly predicted disease transitions in 85.9% of cases across geographically distinct patient populations. The clinicians' lack of prognostic sensitivity, as measured by a false-negative rate of 38.5%, was reduced to 15.4% by the sequential prognostic model. Meaning These findings suggest that an individualized prognostic workflow integrating artificial and human intelligence may facilitate the personalized prevention of psychosis in young patients with clinical high-risk syndromes or recent-onset depression. This prognostic study evaluates whether psychosis transition can be predicted in patients with clinical high-risk syndromes or recent-onset depression by multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging, and polygenic risk scores for schizophrenia.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Koutsouleris, NikolaosUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Dwyer, Dominic B.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Degenhardt, FranziskaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Maj, CarloUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Urquijo-Castro, Maria FernandaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Sanfelici, RacheleUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Popovic, DavidUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Oeztuerk, OemerUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Haas, Shalaila S.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Weiske, JohannaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Ruef, AnneUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kambeitz-Ilankovic, LanaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Antonucci, Linda A.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Neufang, SusanneUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Schmidt-Kraepelin, ChristianUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Ruhrmann, StephanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Penzel, NoraUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kambeitz, JosephUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Haidl, Theresa K.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Rosen, MarleneUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Chisholm, KatharineUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Riecher-Rossler, AnitaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Egloff, LauraUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Schmidt, AndreUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Andreou, ChristinaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Hietala, JarmoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Schirmer, TimoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Romer, GeorgUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Walger, PetraUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Franscini, MauriziaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Traber-Walker, NinaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Schimmelmann, Benno G.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Fluckiger, RahelUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Michel, ChantalUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Rossler, WulfUNSPECIFIEDorcid.org/0000-0003-0049-4533UNSPECIFIED
Borisov, OlegUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Krawitz, Peter M.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Heekeren, KarstenUNSPECIFIEDorcid.org/0000-0001-5105-1922UNSPECIFIED
Buechler, RomanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Pantelis, ChristosUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Falkai, PeterUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Salokangas, Raimo K. R.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Lencer, RebekkaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bertolino, AlessandroUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Borgwardt, StefanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Noethen, MarkusUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Brambilla, PaoloUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Wood, Stephen J.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Upthegrove, RachelUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Schultze-Lutter, FraukeUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Theodoridou, AnastasiaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Meisenzahl, EvaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-308787
DOI: 10.1001/jamapsychiatry.2020.3604
Journal or Publication Title: JAMA Psychiatry
Publisher: AMER MEDICAL ASSOC
Place of Publication: CHICAGO
ISSN: 2168-6238
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
ULTRA-HIGH-RISK; MENTAL STATE; SOCIAL COGNITION; SCHIZOPHRENIA; INDIVIDUALS; DISORDERS; SYMPTOMS; CALCULATOR; DIAGNOSIS; MODELSMultiple languages
PsychiatryMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/30878

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